{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tree_sitter import Language\n",
    "\n",
    "from language_data import LANGUAGE_METADATA\n",
    "from process import DataProcessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "language = 'python'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataProcessor.PARSER.set_language(Language('/src/build/py-tree-sitter-languages.so', language))\n",
    "\n",
    "processor = DataProcessor(language=language,\n",
    "                          language_parser=LANGUAGE_METADATA[language]['language_parser'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependee = 'keras-team/keras'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "definitions = processor.process_dee(dependee, ext=LANGUAGE_METADATA[language]['ext'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>argument_list</th>\n",
       "      <th>docstring</th>\n",
       "      <th>docstring_summary</th>\n",
       "      <th>docstring_tokens</th>\n",
       "      <th>function</th>\n",
       "      <th>function_tokens</th>\n",
       "      <th>identifier</th>\n",
       "      <th>language</th>\n",
       "      <th>nwo</th>\n",
       "      <th>parameters</th>\n",
       "      <th>path</th>\n",
       "      <th>return_statement</th>\n",
       "      <th>sha</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def cnn_layers(x_train_input):\\n    x = layers...</td>\n",
       "      <td>[def, cnn_layers, (, x_train_input, ), :, x, =...</td>\n",
       "      <td>cnn_layers</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x_train_input)</td>\n",
       "      <td>examples/mnist_tfrecord.py</td>\n",
       "      <td>return x_train_out</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def __init__(self, model, steps, metrics_prefi...</td>\n",
       "      <td>[def, __init__, (, self, ,, model, ,, steps, ,...</td>\n",
       "      <td>EvaluateInputTensor.__init__</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, model, steps, metrics_prefix='val', ver...</td>\n",
       "      <td>examples/mnist_tfrecord.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def on_epoch_end(self, epoch, logs={}):\\n     ...</td>\n",
       "      <td>[def, on_epoch_end, (, self, ,, epoch, ,, logs...</td>\n",
       "      <td>EvaluateInputTensor.on_epoch_end</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, epoch, logs={})</td>\n",
       "      <td>examples/mnist_tfrecord.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>Initialize character table.\\n\\n        # Argum...</td>\n",
       "      <td>Initialize character table.</td>\n",
       "      <td>[Initialize, character, table, .]</td>\n",
       "      <td>def __init__(self, chars):\\n        \"\"\"Initial...</td>\n",
       "      <td>[def, __init__, (, self, ,, chars, ), :, self,...</td>\n",
       "      <td>CharacterTable.__init__</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, chars)</td>\n",
       "      <td>examples/addition_rnn.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>One-hot encode given string C.\\n\\n        # Ar...</td>\n",
       "      <td>One-hot encode given string C.</td>\n",
       "      <td>[One, -, hot, encode, given, string, C, .]</td>\n",
       "      <td>def encode(self, C, num_rows):\\n        \"\"\"One...</td>\n",
       "      <td>[def, encode, (, self, ,, C, ,, num_rows, ), :...</td>\n",
       "      <td>CharacterTable.encode</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, C, num_rows)</td>\n",
       "      <td>examples/addition_rnn.py</td>\n",
       "      <td>return x</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>Decode the given vector or 2D array to their c...</td>\n",
       "      <td>Decode the given vector or 2D array to their c...</td>\n",
       "      <td>[Decode, the, given, vector, or, 2D, array, to...</td>\n",
       "      <td>def decode(self, x, calc_argmax=True):\\n      ...</td>\n",
       "      <td>[def, decode, (, self, ,, x, ,, calc_argmax, =...</td>\n",
       "      <td>CharacterTable.decode</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, x, calc_argmax=True)</td>\n",
       "      <td>examples/addition_rnn.py</td>\n",
       "      <td>return ''.join(self.indices_char[x] for x in x)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def generate_movies(n_samples=1200, n_frames=1...</td>\n",
       "      <td>[def, generate_movies, (, n_samples, =, 1200, ...</td>\n",
       "      <td>generate_movies</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(n_samples=1200, n_frames=15)</td>\n",
       "      <td>examples/conv_lstm.py</td>\n",
       "      <td>return noisy_movies, shifted_movies</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def preprocess_image(image_path):\\n    # Util ...</td>\n",
       "      <td>[def, preprocess_image, (, image_path, ), :, #...</td>\n",
       "      <td>preprocess_image</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(image_path)</td>\n",
       "      <td>examples/deep_dream.py</td>\n",
       "      <td>return img</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def deprocess_image(x):\\n    # Util function t...</td>\n",
       "      <td>[def, deprocess_image, (, x, ), :, # Util func...</td>\n",
       "      <td>deprocess_image</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x)</td>\n",
       "      <td>examples/deep_dream.py</td>\n",
       "      <td>return x</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def eval_loss_and_grads(x):\\n    outs = fetch_...</td>\n",
       "      <td>[def, eval_loss_and_grads, (, x, ), :, outs, =...</td>\n",
       "      <td>eval_loss_and_grads</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x)</td>\n",
       "      <td>examples/deep_dream.py</td>\n",
       "      <td>return loss_value, grad_values</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def resize_img(img, size):\\n    img = np.copy(...</td>\n",
       "      <td>[def, resize_img, (, img, ,, size, ), :, img, ...</td>\n",
       "      <td>resize_img</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(img, size)</td>\n",
       "      <td>examples/deep_dream.py</td>\n",
       "      <td>return scipy.ndimage.zoom(img, factors, order=1)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def gradient_ascent(x, iterations, step, max_l...</td>\n",
       "      <td>[def, gradient_ascent, (, x, ,, iterations, ,,...</td>\n",
       "      <td>gradient_ascent</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x, iterations, step, max_loss=None)</td>\n",
       "      <td>examples/deep_dream.py</td>\n",
       "      <td>return x</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def load_img(fname, input_size, preprocess_fn)...</td>\n",
       "      <td>[def, load_img, (, fname, ,, input_size, ,, pr...</td>\n",
       "      <td>load_img</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(fname, input_size, preprocess_fn)</td>\n",
       "      <td>examples/class_activation_maps.py</td>\n",
       "      <td>return imgs, original_img, original_size</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def get_cam_model(model_class,\\n              ...</td>\n",
       "      <td>[def, get_cam_model, (, model_class, ,, input_...</td>\n",
       "      <td>get_cam_model</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(model_class,\\n                  input_size=22...</td>\n",
       "      <td>examples/class_activation_maps.py</td>\n",
       "      <td>return cam_model</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def postprocess(preds, cams, top_k=1):\\n    id...</td>\n",
       "      <td>[def, postprocess, (, preds, ,, cams, ,, top_k...</td>\n",
       "      <td>postprocess</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(preds, cams, top_k=1)</td>\n",
       "      <td>examples/class_activation_maps.py</td>\n",
       "      <td>return class_activation_map</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def preprocess_image(image_path):\\n    img = l...</td>\n",
       "      <td>[def, preprocess_image, (, image_path, ), :, i...</td>\n",
       "      <td>preprocess_image</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(image_path)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return img</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def deprocess_image(x):\\n    if K.image_data_f...</td>\n",
       "      <td>[def, deprocess_image, (, x, ), :, if, K, ., i...</td>\n",
       "      <td>deprocess_image</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return x</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def gram_matrix(x):\\n    assert K.ndim(x) == 3...</td>\n",
       "      <td>[def, gram_matrix, (, x, ), :, assert, K, ., n...</td>\n",
       "      <td>gram_matrix</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return gram</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def style_loss(style, combination):\\n    asser...</td>\n",
       "      <td>[def, style_loss, (, style, ,, combination, ),...</td>\n",
       "      <td>style_loss</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(style, combination)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return K.sum(K.square(S - C)) / (4.0 * (channe...</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def content_loss(base, combination):\\n    retu...</td>\n",
       "      <td>[def, content_loss, (, base, ,, combination, )...</td>\n",
       "      <td>content_loss</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(base, combination)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return K.sum(K.square(combination - base))</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def total_variation_loss(x):\\n    assert K.ndi...</td>\n",
       "      <td>[def, total_variation_loss, (, x, ), :, assert...</td>\n",
       "      <td>total_variation_loss</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return K.sum(K.pow(a + b, 1.25))</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def eval_loss_and_grads(x):\\n    if K.image_da...</td>\n",
       "      <td>[def, eval_loss_and_grads, (, x, ), :, if, K, ...</td>\n",
       "      <td>eval_loss_and_grads</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return loss_value, grad_values</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def __init__(self):\\n        self.loss_value =...</td>\n",
       "      <td>[def, __init__, (, self, ), :, self, ., loss_v...</td>\n",
       "      <td>Evaluator.__init__</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def loss(self, x):\\n        assert self.loss_v...</td>\n",
       "      <td>[def, loss, (, self, ,, x, ), :, assert, self,...</td>\n",
       "      <td>Evaluator.loss</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, x)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return self.loss_value</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def grads(self, x):\\n        assert self.loss_...</td>\n",
       "      <td>[def, grads, (, self, ,, x, ), :, assert, self...</td>\n",
       "      <td>Evaluator.grads</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, x)</td>\n",
       "      <td>examples/neural_style_transfer.py</td>\n",
       "      <td>return grad_values</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td></td>\n",
       "      <td>The Squashing Function.\\n    The nonlinear act...</td>\n",
       "      <td>The Squashing Function.\\n    The nonlinear act...</td>\n",
       "      <td>[The, Squashing, Function, ., The, nonlinear, ...</td>\n",
       "      <td>def squash(x, axis=-1):\\n    \"\"\"The Squashing ...</td>\n",
       "      <td>[def, squash, (, x, ,, axis, =, -, 1, ), :, s_...</td>\n",
       "      <td>squash</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(x, axis=-1)</td>\n",
       "      <td>examples/cifar10_cnn_capsule.py</td>\n",
       "      <td>return scale * x</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td></td>\n",
       "      <td>Margin loss\\n\\n    # Arguments\\n        y_true...</td>\n",
       "      <td>Margin loss</td>\n",
       "      <td>[Margin, loss]</td>\n",
       "      <td>def margin_loss(y_true, y_pred):\\n    \"\"\"Margi...</td>\n",
       "      <td>[def, margin_loss, (, y_true, ,, y_pred, ), :,...</td>\n",
       "      <td>margin_loss</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(y_true, y_pred)</td>\n",
       "      <td>examples/cifar10_cnn_capsule.py</td>\n",
       "      <td>return K.sum(y_true * K.square(K.relu(1 - marg...</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def __init__(self,\\n                 num_capsu...</td>\n",
       "      <td>[def, __init__, (, self, ,, num_capsule, ,, di...</td>\n",
       "      <td>Capsule.__init__</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self,\\n                 num_capsule,\\n       ...</td>\n",
       "      <td>examples/cifar10_cnn_capsule.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def build(self, input_shape):\\n        input_d...</td>\n",
       "      <td>[def, build, (, self, ,, input_shape, ), :, in...</td>\n",
       "      <td>Capsule.build</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, input_shape)</td>\n",
       "      <td>examples/cifar10_cnn_capsule.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td></td>\n",
       "      <td>Following the routing algorithm from Hinton's ...</td>\n",
       "      <td>Following the routing algorithm from Hinton's ...</td>\n",
       "      <td>[Following, the, routing, algorithm, from, Hin...</td>\n",
       "      <td>def call(self, inputs, **kwargs):\\n        \"\"\"...</td>\n",
       "      <td>[def, call, (, self, ,, inputs, ,, *, *, kwarg...</td>\n",
       "      <td>Capsule.call</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, inputs, **kwargs)</td>\n",
       "      <td>examples/cifar10_cnn_capsule.py</td>\n",
       "      <td>return o</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td></td>\n",
       "      <td>Returns the mean accuracy on the given test da...</td>\n",
       "      <td>Returns the mean accuracy on the given test da...</td>\n",
       "      <td>[Returns, the, mean, accuracy, on, the, given,...</td>\n",
       "      <td>def score(self, x, y, **kwargs):\\n        \"\"\"R...</td>\n",
       "      <td>[def, score, (, self, ,, x, ,, y, ,, *, *, kwa...</td>\n",
       "      <td>KerasClassifier.score</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, x, y, **kwargs)</td>\n",
       "      <td>keras/wrappers/scikit_learn.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td></td>\n",
       "      <td>Returns predictions for the given test data.\\n...</td>\n",
       "      <td>Returns predictions for the given test data.</td>\n",
       "      <td>[Returns, predictions, for, the, given, test, ...</td>\n",
       "      <td>def predict(self, x, **kwargs):\\n        \"\"\"Re...</td>\n",
       "      <td>[def, predict, (, self, ,, x, ,, *, *, kwargs,...</td>\n",
       "      <td>KerasRegressor.predict</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, x, **kwargs)</td>\n",
       "      <td>keras/wrappers/scikit_learn.py</td>\n",
       "      <td>return np.squeeze(self.model.predict(x, **kwar...</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td></td>\n",
       "      <td>Returns the mean loss on the given test data a...</td>\n",
       "      <td>Returns the mean loss on the given test data a...</td>\n",
       "      <td>[Returns, the, mean, loss, on, the, given, tes...</td>\n",
       "      <td>def score(self, x, y, **kwargs):\\n        \"\"\"R...</td>\n",
       "      <td>[def, score, (, self, ,, x, ,, y, ,, *, *, kwa...</td>\n",
       "      <td>KerasRegressor.score</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(self, x, y, **kwargs)</td>\n",
       "      <td>keras/wrappers/scikit_learn.py</td>\n",
       "      <td>return -loss</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td></td>\n",
       "      <td>Loads the Fashion-MNIST dataset.\\n\\n    # Retu...</td>\n",
       "      <td>Loads the Fashion-MNIST dataset.</td>\n",
       "      <td>[Loads, the, Fashion, -, MNIST, dataset, .]</td>\n",
       "      <td>def load_data():\\n    \"\"\"Loads the Fashion-MNI...</td>\n",
       "      <td>[def, load_data, (, ), :, dirname, =, os, ., p...</td>\n",
       "      <td>load_data</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>()</td>\n",
       "      <td>keras/datasets/fashion_mnist.py</td>\n",
       "      <td>return (x_train, y_train), (x_test, y_test)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td></td>\n",
       "      <td>Loads the IMDB dataset.\\n\\n    # Arguments\\n  ...</td>\n",
       "      <td>Loads the IMDB dataset.</td>\n",
       "      <td>[Loads, the, IMDB, dataset, .]</td>\n",
       "      <td>def load_data(path='imdb.npz', num_words=None,...</td>\n",
       "      <td>[def, load_data, (, path, =, 'imdb.npz', ,, nu...</td>\n",
       "      <td>load_data</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(path='imdb.npz', num_words=None, skip_top=0,\\...</td>\n",
       "      <td>keras/datasets/imdb.py</td>\n",
       "      <td>return (x_train, y_train), (x_test, y_test)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td></td>\n",
       "      <td>Retrieves the dictionary mapping words to word...</td>\n",
       "      <td>Retrieves the dictionary mapping words to word...</td>\n",
       "      <td>[Retrieves, the, dictionary, mapping, words, t...</td>\n",
       "      <td>def get_word_index(path='imdb_word_index.json'...</td>\n",
       "      <td>[def, get_word_index, (, path, =, 'imdb_word_i...</td>\n",
       "      <td>get_word_index</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(path='imdb_word_index.json')</td>\n",
       "      <td>keras/datasets/imdb.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1769</th>\n",
       "      <td></td>\n",
       "      <td>Loads CIFAR100 dataset.\\n\\n    # Arguments\\n  ...</td>\n",
       "      <td>Loads CIFAR100 dataset.</td>\n",
       "      <td>[Loads, CIFAR100, dataset, .]</td>\n",
       "      <td>def load_data(label_mode='fine'):\\n    \"\"\"Load...</td>\n",
       "      <td>[def, load_data, (, label_mode, =, 'fine', ), ...</td>\n",
       "      <td>load_data</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(label_mode='fine')</td>\n",
       "      <td>keras/datasets/cifar100.py</td>\n",
       "      <td>return (x_train, y_train), (x_test, y_test)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td></td>\n",
       "      <td>Loads the Boston Housing dataset.\\n\\n    # Arg...</td>\n",
       "      <td>Loads the Boston Housing dataset.</td>\n",
       "      <td>[Loads, the, Boston, Housing, dataset, .]</td>\n",
       "      <td>def load_data(path='boston_housing.npz', test_...</td>\n",
       "      <td>[def, load_data, (, path, =, 'boston_housing.n...</td>\n",
       "      <td>load_data</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(path='boston_housing.npz', test_split=0.2, se...</td>\n",
       "      <td>keras/datasets/boston_housing.py</td>\n",
       "      <td>return (x_train, y_train), (x_test, y_test)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td></td>\n",
       "      <td>Loads the MNIST dataset.\\n\\n    # Arguments\\n ...</td>\n",
       "      <td>Loads the MNIST dataset.</td>\n",
       "      <td>[Loads, the, MNIST, dataset, .]</td>\n",
       "      <td>def load_data(path='mnist.npz'):\\n    \"\"\"Loads...</td>\n",
       "      <td>[def, load_data, (, path, =, 'mnist.npz', ), :...</td>\n",
       "      <td>load_data</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(path='mnist.npz')</td>\n",
       "      <td>keras/datasets/mnist.py</td>\n",
       "      <td>return (x_train, y_train), (x_test, y_test)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1772</th>\n",
       "      <td></td>\n",
       "      <td>Internal utility for parsing CIFAR data.\\n\\n  ...</td>\n",
       "      <td>Internal utility for parsing CIFAR data.</td>\n",
       "      <td>[Internal, utility, for, parsing, CIFAR, data, .]</td>\n",
       "      <td>def load_batch(fpath, label_key='labels'):\\n  ...</td>\n",
       "      <td>[def, load_batch, (, fpath, ,, label_key, =, '...</td>\n",
       "      <td>load_batch</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(fpath, label_key='labels')</td>\n",
       "      <td>keras/datasets/cifar.py</td>\n",
       "      <td>return data, labels</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1773</th>\n",
       "      <td></td>\n",
       "      <td>Loads CIFAR10 dataset.\\n\\n    # Returns\\n     ...</td>\n",
       "      <td>Loads CIFAR10 dataset.</td>\n",
       "      <td>[Loads, CIFAR10, dataset, .]</td>\n",
       "      <td>def load_data():\\n    \"\"\"Loads CIFAR10 dataset...</td>\n",
       "      <td>[def, load_data, (, ), :, dirname, =, 'cifar-1...</td>\n",
       "      <td>load_data</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>()</td>\n",
       "      <td>keras/datasets/cifar10.py</td>\n",
       "      <td>return (x_train, y_train), (x_test, y_test)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td></td>\n",
       "      <td>Loads the Reuters newswire classification data...</td>\n",
       "      <td>Loads the Reuters newswire classification data...</td>\n",
       "      <td>[Loads, the, Reuters, newswire, classification...</td>\n",
       "      <td>def load_data(path='reuters.npz', num_words=No...</td>\n",
       "      <td>[def, load_data, (, path, =, 'reuters.npz', ,,...</td>\n",
       "      <td>load_data</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(path='reuters.npz', num_words=None, skip_top=...</td>\n",
       "      <td>keras/datasets/reuters.py</td>\n",
       "      <td>return (x_train, y_train), (x_test, y_test)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td></td>\n",
       "      <td>Retrieves the dictionary mapping words to word...</td>\n",
       "      <td>Retrieves the dictionary mapping words to word...</td>\n",
       "      <td>[Retrieves, the, dictionary, mapping, words, t...</td>\n",
       "      <td>def get_word_index(path='reuters_word_index.js...</td>\n",
       "      <td>[def, get_word_index, (, path, =, 'reuters_wor...</td>\n",
       "      <td>get_word_index</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(path='reuters_word_index.json')</td>\n",
       "      <td>keras/datasets/reuters.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1776</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def get_function_signature(function, method=Tr...</td>\n",
       "      <td>[def, get_function_signature, (, function, ,, ...</td>\n",
       "      <td>get_function_signature</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(function, method=True)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return post_process_signature(signature)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1777</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def get_class_signature(cls):\\n    try:\\n     ...</td>\n",
       "      <td>[def, get_class_signature, (, cls, ), :, try, ...</td>\n",
       "      <td>get_class_signature</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(cls)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return post_process_signature(class_signature)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def post_process_signature(signature):\\n    pa...</td>\n",
       "      <td>[def, post_process_signature, (, signature, ),...</td>\n",
       "      <td>post_process_signature</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(signature)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return signature</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def clean_module_name(name):\\n    if name.star...</td>\n",
       "      <td>[def, clean_module_name, (, name, ), :, if, na...</td>\n",
       "      <td>clean_module_name</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(name)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return name</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def class_to_source_link(cls):\\n    module_nam...</td>\n",
       "      <td>[def, class_to_source_link, (, cls, ), :, modu...</td>\n",
       "      <td>class_to_source_link</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(cls)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return '[[source]](' + link + ')'</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1781</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def code_snippet(snippet):\\n    result = '```p...</td>\n",
       "      <td>[def, code_snippet, (, snippet, ), :, result, ...</td>\n",
       "      <td>code_snippet</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(snippet)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return result</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def count_leading_spaces(s):\\n    ws = re.sear...</td>\n",
       "      <td>[def, count_leading_spaces, (, s, ), :, ws, =,...</td>\n",
       "      <td>count_leading_spaces</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(s)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def process_list_block(docstring, starting_poi...</td>\n",
       "      <td>[def, process_list_block, (, docstring, ,, sta...</td>\n",
       "      <td>process_list_block</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(docstring, starting_point, section_end,\\n    ...</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return docstring, block</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def process_docstring(docstring):\\n    # First...</td>\n",
       "      <td>[def, process_docstring, (, docstring, ), :, #...</td>\n",
       "      <td>process_docstring</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(docstring)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return docstring</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def add_np_implementation(function, docstring)...</td>\n",
       "      <td>[def, add_np_implementation, (, function, ,, d...</td>\n",
       "      <td>add_np_implementation</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(function, docstring)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return docstring.replace('{{np_implementation}...</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1786</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def read_file(path):\\n    with open(path) as f...</td>\n",
       "      <td>[def, read_file, (, path, ), :, with, open, (,...</td>\n",
       "      <td>read_file</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(path)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1787</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def collect_class_methods(cls, methods):\\n    ...</td>\n",
       "      <td>[def, collect_class_methods, (, cls, ,, method...</td>\n",
       "      <td>collect_class_methods</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(cls, methods)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return methods</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1788</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def render_function(function, method=True):\\n ...</td>\n",
       "      <td>[def, render_function, (, function, ,, method,...</td>\n",
       "      <td>render_function</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(function, method=True)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return '\\n\\n'.join(subblocks)</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>def read_page_data(page_data, type):\\n    asse...</td>\n",
       "      <td>[def, read_page_data, (, page_data, ,, type, )...</td>\n",
       "      <td>read_page_data</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(page_data, type)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return data</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td></td>\n",
       "      <td>Extract the module docstring.\\n\\n    Also find...</td>\n",
       "      <td>Extract the module docstring.</td>\n",
       "      <td>[Extract, the, module, docstring, .]</td>\n",
       "      <td>def get_module_docstring(filepath):\\n    \"\"\"Ex...</td>\n",
       "      <td>[def, get_module_docstring, (, filepath, ), :,...</td>\n",
       "      <td>get_module_docstring</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(filepath)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td>return docstring, co.co_firstlineno</td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td></td>\n",
       "      <td>Copy the examples directory in the documentati...</td>\n",
       "      <td>Copy the examples directory in the documentation.</td>\n",
       "      <td>[Copy, the, examples, directory, in, the, docu...</td>\n",
       "      <td>def copy_examples(examples_dir, destination_di...</td>\n",
       "      <td>[def, copy_examples, (, examples_dir, ,, desti...</td>\n",
       "      <td>copy_examples</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(examples_dir, destination_dir)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td></td>\n",
       "      <td>Generates the markdown files for the documenta...</td>\n",
       "      <td>Generates the markdown files for the documenta...</td>\n",
       "      <td>[Generates, the, markdown, files, for, the, do...</td>\n",
       "      <td>def generate(sources_dir):\\n    \"\"\"Generates t...</td>\n",
       "      <td>[def, generate, (, sources_dir, ), :, template...</td>\n",
       "      <td>generate</td>\n",
       "      <td>python</td>\n",
       "      <td>keras-team/keras</td>\n",
       "      <td>(sources_dir)</td>\n",
       "      <td>docs/autogen.py</td>\n",
       "      <td></td>\n",
       "      <td>0fc33feb5f4efe3bb823c57a8390f52932a966ab</td>\n",
       "      <td>https://github.com/keras-team/keras/blob/0fc33...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1793 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     argument_list                                          docstring  \\\n",
       "0                                                                       \n",
       "1                                                                       \n",
       "2                                                                       \n",
       "3                   Initialize character table.\\n\\n        # Argum...   \n",
       "4                   One-hot encode given string C.\\n\\n        # Ar...   \n",
       "5                   Decode the given vector or 2D array to their c...   \n",
       "6                                                                       \n",
       "7                                                                       \n",
       "8                                                                       \n",
       "9                                                                       \n",
       "10                                                                      \n",
       "11                                                                      \n",
       "12                                                                      \n",
       "13                                                                      \n",
       "14                                                                      \n",
       "15                                                                      \n",
       "16                                                                      \n",
       "17                                                                      \n",
       "18                                                                      \n",
       "19                                                                      \n",
       "20                                                                      \n",
       "21                                                                      \n",
       "22                                                                      \n",
       "23                                                                      \n",
       "24                                                                      \n",
       "25                  The Squashing Function.\\n    The nonlinear act...   \n",
       "26                  Margin loss\\n\\n    # Arguments\\n        y_true...   \n",
       "27                                                                      \n",
       "28                                                                      \n",
       "29                  Following the routing algorithm from Hinton's ...   \n",
       "...            ...                                                ...   \n",
       "1763                Returns the mean accuracy on the given test da...   \n",
       "1764                Returns predictions for the given test data.\\n...   \n",
       "1765                Returns the mean loss on the given test data a...   \n",
       "1766                Loads the Fashion-MNIST dataset.\\n\\n    # Retu...   \n",
       "1767                Loads the IMDB dataset.\\n\\n    # Arguments\\n  ...   \n",
       "1768                Retrieves the dictionary mapping words to word...   \n",
       "1769                Loads CIFAR100 dataset.\\n\\n    # Arguments\\n  ...   \n",
       "1770                Loads the Boston Housing dataset.\\n\\n    # Arg...   \n",
       "1771                Loads the MNIST dataset.\\n\\n    # Arguments\\n ...   \n",
       "1772                Internal utility for parsing CIFAR data.\\n\\n  ...   \n",
       "1773                Loads CIFAR10 dataset.\\n\\n    # Returns\\n     ...   \n",
       "1774                Loads the Reuters newswire classification data...   \n",
       "1775                Retrieves the dictionary mapping words to word...   \n",
       "1776                                                                    \n",
       "1777                                                                    \n",
       "1778                                                                    \n",
       "1779                                                                    \n",
       "1780                                                                    \n",
       "1781                                                                    \n",
       "1782                                                                    \n",
       "1783                                                                    \n",
       "1784                                                                    \n",
       "1785                                                                    \n",
       "1786                                                                    \n",
       "1787                                                                    \n",
       "1788                                                                    \n",
       "1789                                                                    \n",
       "1790                Extract the module docstring.\\n\\n    Also find...   \n",
       "1791                Copy the examples directory in the documentati...   \n",
       "1792                Generates the markdown files for the documenta...   \n",
       "\n",
       "                                      docstring_summary  \\\n",
       "0                                                         \n",
       "1                                                         \n",
       "2                                                         \n",
       "3                           Initialize character table.   \n",
       "4                        One-hot encode given string C.   \n",
       "5     Decode the given vector or 2D array to their c...   \n",
       "6                                                         \n",
       "7                                                         \n",
       "8                                                         \n",
       "9                                                         \n",
       "10                                                        \n",
       "11                                                        \n",
       "12                                                        \n",
       "13                                                        \n",
       "14                                                        \n",
       "15                                                        \n",
       "16                                                        \n",
       "17                                                        \n",
       "18                                                        \n",
       "19                                                        \n",
       "20                                                        \n",
       "21                                                        \n",
       "22                                                        \n",
       "23                                                        \n",
       "24                                                        \n",
       "25    The Squashing Function.\\n    The nonlinear act...   \n",
       "26                                          Margin loss   \n",
       "27                                                        \n",
       "28                                                        \n",
       "29    Following the routing algorithm from Hinton's ...   \n",
       "...                                                 ...   \n",
       "1763  Returns the mean accuracy on the given test da...   \n",
       "1764       Returns predictions for the given test data.   \n",
       "1765  Returns the mean loss on the given test data a...   \n",
       "1766                   Loads the Fashion-MNIST dataset.   \n",
       "1767                            Loads the IMDB dataset.   \n",
       "1768  Retrieves the dictionary mapping words to word...   \n",
       "1769                            Loads CIFAR100 dataset.   \n",
       "1770                  Loads the Boston Housing dataset.   \n",
       "1771                           Loads the MNIST dataset.   \n",
       "1772           Internal utility for parsing CIFAR data.   \n",
       "1773                             Loads CIFAR10 dataset.   \n",
       "1774  Loads the Reuters newswire classification data...   \n",
       "1775  Retrieves the dictionary mapping words to word...   \n",
       "1776                                                      \n",
       "1777                                                      \n",
       "1778                                                      \n",
       "1779                                                      \n",
       "1780                                                      \n",
       "1781                                                      \n",
       "1782                                                      \n",
       "1783                                                      \n",
       "1784                                                      \n",
       "1785                                                      \n",
       "1786                                                      \n",
       "1787                                                      \n",
       "1788                                                      \n",
       "1789                                                      \n",
       "1790                      Extract the module docstring.   \n",
       "1791  Copy the examples directory in the documentation.   \n",
       "1792  Generates the markdown files for the documenta...   \n",
       "\n",
       "                                       docstring_tokens  \\\n",
       "0                                                    []   \n",
       "1                                                    []   \n",
       "2                                                    []   \n",
       "3                     [Initialize, character, table, .]   \n",
       "4            [One, -, hot, encode, given, string, C, .]   \n",
       "5     [Decode, the, given, vector, or, 2D, array, to...   \n",
       "6                                                    []   \n",
       "7                                                    []   \n",
       "8                                                    []   \n",
       "9                                                    []   \n",
       "10                                                   []   \n",
       "11                                                   []   \n",
       "12                                                   []   \n",
       "13                                                   []   \n",
       "14                                                   []   \n",
       "15                                                   []   \n",
       "16                                                   []   \n",
       "17                                                   []   \n",
       "18                                                   []   \n",
       "19                                                   []   \n",
       "20                                                   []   \n",
       "21                                                   []   \n",
       "22                                                   []   \n",
       "23                                                   []   \n",
       "24                                                   []   \n",
       "25    [The, Squashing, Function, ., The, nonlinear, ...   \n",
       "26                                       [Margin, loss]   \n",
       "27                                                   []   \n",
       "28                                                   []   \n",
       "29    [Following, the, routing, algorithm, from, Hin...   \n",
       "...                                                 ...   \n",
       "1763  [Returns, the, mean, accuracy, on, the, given,...   \n",
       "1764  [Returns, predictions, for, the, given, test, ...   \n",
       "1765  [Returns, the, mean, loss, on, the, given, tes...   \n",
       "1766        [Loads, the, Fashion, -, MNIST, dataset, .]   \n",
       "1767                     [Loads, the, IMDB, dataset, .]   \n",
       "1768  [Retrieves, the, dictionary, mapping, words, t...   \n",
       "1769                      [Loads, CIFAR100, dataset, .]   \n",
       "1770          [Loads, the, Boston, Housing, dataset, .]   \n",
       "1771                    [Loads, the, MNIST, dataset, .]   \n",
       "1772  [Internal, utility, for, parsing, CIFAR, data, .]   \n",
       "1773                       [Loads, CIFAR10, dataset, .]   \n",
       "1774  [Loads, the, Reuters, newswire, classification...   \n",
       "1775  [Retrieves, the, dictionary, mapping, words, t...   \n",
       "1776                                                 []   \n",
       "1777                                                 []   \n",
       "1778                                                 []   \n",
       "1779                                                 []   \n",
       "1780                                                 []   \n",
       "1781                                                 []   \n",
       "1782                                                 []   \n",
       "1783                                                 []   \n",
       "1784                                                 []   \n",
       "1785                                                 []   \n",
       "1786                                                 []   \n",
       "1787                                                 []   \n",
       "1788                                                 []   \n",
       "1789                                                 []   \n",
       "1790               [Extract, the, module, docstring, .]   \n",
       "1791  [Copy, the, examples, directory, in, the, docu...   \n",
       "1792  [Generates, the, markdown, files, for, the, do...   \n",
       "\n",
       "                                               function  \\\n",
       "0     def cnn_layers(x_train_input):\\n    x = layers...   \n",
       "1     def __init__(self, model, steps, metrics_prefi...   \n",
       "2     def on_epoch_end(self, epoch, logs={}):\\n     ...   \n",
       "3     def __init__(self, chars):\\n        \"\"\"Initial...   \n",
       "4     def encode(self, C, num_rows):\\n        \"\"\"One...   \n",
       "5     def decode(self, x, calc_argmax=True):\\n      ...   \n",
       "6     def generate_movies(n_samples=1200, n_frames=1...   \n",
       "7     def preprocess_image(image_path):\\n    # Util ...   \n",
       "8     def deprocess_image(x):\\n    # Util function t...   \n",
       "9     def eval_loss_and_grads(x):\\n    outs = fetch_...   \n",
       "10    def resize_img(img, size):\\n    img = np.copy(...   \n",
       "11    def gradient_ascent(x, iterations, step, max_l...   \n",
       "12    def load_img(fname, input_size, preprocess_fn)...   \n",
       "13    def get_cam_model(model_class,\\n              ...   \n",
       "14    def postprocess(preds, cams, top_k=1):\\n    id...   \n",
       "15    def preprocess_image(image_path):\\n    img = l...   \n",
       "16    def deprocess_image(x):\\n    if K.image_data_f...   \n",
       "17    def gram_matrix(x):\\n    assert K.ndim(x) == 3...   \n",
       "18    def style_loss(style, combination):\\n    asser...   \n",
       "19    def content_loss(base, combination):\\n    retu...   \n",
       "20    def total_variation_loss(x):\\n    assert K.ndi...   \n",
       "21    def eval_loss_and_grads(x):\\n    if K.image_da...   \n",
       "22    def __init__(self):\\n        self.loss_value =...   \n",
       "23    def loss(self, x):\\n        assert self.loss_v...   \n",
       "24    def grads(self, x):\\n        assert self.loss_...   \n",
       "25    def squash(x, axis=-1):\\n    \"\"\"The Squashing ...   \n",
       "26    def margin_loss(y_true, y_pred):\\n    \"\"\"Margi...   \n",
       "27    def __init__(self,\\n                 num_capsu...   \n",
       "28    def build(self, input_shape):\\n        input_d...   \n",
       "29    def call(self, inputs, **kwargs):\\n        \"\"\"...   \n",
       "...                                                 ...   \n",
       "1763  def score(self, x, y, **kwargs):\\n        \"\"\"R...   \n",
       "1764  def predict(self, x, **kwargs):\\n        \"\"\"Re...   \n",
       "1765  def score(self, x, y, **kwargs):\\n        \"\"\"R...   \n",
       "1766  def load_data():\\n    \"\"\"Loads the Fashion-MNI...   \n",
       "1767  def load_data(path='imdb.npz', num_words=None,...   \n",
       "1768  def get_word_index(path='imdb_word_index.json'...   \n",
       "1769  def load_data(label_mode='fine'):\\n    \"\"\"Load...   \n",
       "1770  def load_data(path='boston_housing.npz', test_...   \n",
       "1771  def load_data(path='mnist.npz'):\\n    \"\"\"Loads...   \n",
       "1772  def load_batch(fpath, label_key='labels'):\\n  ...   \n",
       "1773  def load_data():\\n    \"\"\"Loads CIFAR10 dataset...   \n",
       "1774  def load_data(path='reuters.npz', num_words=No...   \n",
       "1775  def get_word_index(path='reuters_word_index.js...   \n",
       "1776  def get_function_signature(function, method=Tr...   \n",
       "1777  def get_class_signature(cls):\\n    try:\\n     ...   \n",
       "1778  def post_process_signature(signature):\\n    pa...   \n",
       "1779  def clean_module_name(name):\\n    if name.star...   \n",
       "1780  def class_to_source_link(cls):\\n    module_nam...   \n",
       "1781  def code_snippet(snippet):\\n    result = '```p...   \n",
       "1782  def count_leading_spaces(s):\\n    ws = re.sear...   \n",
       "1783  def process_list_block(docstring, starting_poi...   \n",
       "1784  def process_docstring(docstring):\\n    # First...   \n",
       "1785  def add_np_implementation(function, docstring)...   \n",
       "1786  def read_file(path):\\n    with open(path) as f...   \n",
       "1787  def collect_class_methods(cls, methods):\\n    ...   \n",
       "1788  def render_function(function, method=True):\\n ...   \n",
       "1789  def read_page_data(page_data, type):\\n    asse...   \n",
       "1790  def get_module_docstring(filepath):\\n    \"\"\"Ex...   \n",
       "1791  def copy_examples(examples_dir, destination_di...   \n",
       "1792  def generate(sources_dir):\\n    \"\"\"Generates t...   \n",
       "\n",
       "                                        function_tokens  \\\n",
       "0     [def, cnn_layers, (, x_train_input, ), :, x, =...   \n",
       "1     [def, __init__, (, self, ,, model, ,, steps, ,...   \n",
       "2     [def, on_epoch_end, (, self, ,, epoch, ,, logs...   \n",
       "3     [def, __init__, (, self, ,, chars, ), :, self,...   \n",
       "4     [def, encode, (, self, ,, C, ,, num_rows, ), :...   \n",
       "5     [def, decode, (, self, ,, x, ,, calc_argmax, =...   \n",
       "6     [def, generate_movies, (, n_samples, =, 1200, ...   \n",
       "7     [def, preprocess_image, (, image_path, ), :, #...   \n",
       "8     [def, deprocess_image, (, x, ), :, # Util func...   \n",
       "9     [def, eval_loss_and_grads, (, x, ), :, outs, =...   \n",
       "10    [def, resize_img, (, img, ,, size, ), :, img, ...   \n",
       "11    [def, gradient_ascent, (, x, ,, iterations, ,,...   \n",
       "12    [def, load_img, (, fname, ,, input_size, ,, pr...   \n",
       "13    [def, get_cam_model, (, model_class, ,, input_...   \n",
       "14    [def, postprocess, (, preds, ,, cams, ,, top_k...   \n",
       "15    [def, preprocess_image, (, image_path, ), :, i...   \n",
       "16    [def, deprocess_image, (, x, ), :, if, K, ., i...   \n",
       "17    [def, gram_matrix, (, x, ), :, assert, K, ., n...   \n",
       "18    [def, style_loss, (, style, ,, combination, ),...   \n",
       "19    [def, content_loss, (, base, ,, combination, )...   \n",
       "20    [def, total_variation_loss, (, x, ), :, assert...   \n",
       "21    [def, eval_loss_and_grads, (, x, ), :, if, K, ...   \n",
       "22    [def, __init__, (, self, ), :, self, ., loss_v...   \n",
       "23    [def, loss, (, self, ,, x, ), :, assert, self,...   \n",
       "24    [def, grads, (, self, ,, x, ), :, assert, self...   \n",
       "25    [def, squash, (, x, ,, axis, =, -, 1, ), :, s_...   \n",
       "26    [def, margin_loss, (, y_true, ,, y_pred, ), :,...   \n",
       "27    [def, __init__, (, self, ,, num_capsule, ,, di...   \n",
       "28    [def, build, (, self, ,, input_shape, ), :, in...   \n",
       "29    [def, call, (, self, ,, inputs, ,, *, *, kwarg...   \n",
       "...                                                 ...   \n",
       "1763  [def, score, (, self, ,, x, ,, y, ,, *, *, kwa...   \n",
       "1764  [def, predict, (, self, ,, x, ,, *, *, kwargs,...   \n",
       "1765  [def, score, (, self, ,, x, ,, y, ,, *, *, kwa...   \n",
       "1766  [def, load_data, (, ), :, dirname, =, os, ., p...   \n",
       "1767  [def, load_data, (, path, =, 'imdb.npz', ,, nu...   \n",
       "1768  [def, get_word_index, (, path, =, 'imdb_word_i...   \n",
       "1769  [def, load_data, (, label_mode, =, 'fine', ), ...   \n",
       "1770  [def, load_data, (, path, =, 'boston_housing.n...   \n",
       "1771  [def, load_data, (, path, =, 'mnist.npz', ), :...   \n",
       "1772  [def, load_batch, (, fpath, ,, label_key, =, '...   \n",
       "1773  [def, load_data, (, ), :, dirname, =, 'cifar-1...   \n",
       "1774  [def, load_data, (, path, =, 'reuters.npz', ,,...   \n",
       "1775  [def, get_word_index, (, path, =, 'reuters_wor...   \n",
       "1776  [def, get_function_signature, (, function, ,, ...   \n",
       "1777  [def, get_class_signature, (, cls, ), :, try, ...   \n",
       "1778  [def, post_process_signature, (, signature, ),...   \n",
       "1779  [def, clean_module_name, (, name, ), :, if, na...   \n",
       "1780  [def, class_to_source_link, (, cls, ), :, modu...   \n",
       "1781  [def, code_snippet, (, snippet, ), :, result, ...   \n",
       "1782  [def, count_leading_spaces, (, s, ), :, ws, =,...   \n",
       "1783  [def, process_list_block, (, docstring, ,, sta...   \n",
       "1784  [def, process_docstring, (, docstring, ), :, #...   \n",
       "1785  [def, add_np_implementation, (, function, ,, d...   \n",
       "1786  [def, read_file, (, path, ), :, with, open, (,...   \n",
       "1787  [def, collect_class_methods, (, cls, ,, method...   \n",
       "1788  [def, render_function, (, function, ,, method,...   \n",
       "1789  [def, read_page_data, (, page_data, ,, type, )...   \n",
       "1790  [def, get_module_docstring, (, filepath, ), :,...   \n",
       "1791  [def, copy_examples, (, examples_dir, ,, desti...   \n",
       "1792  [def, generate, (, sources_dir, ), :, template...   \n",
       "\n",
       "                            identifier language               nwo  \\\n",
       "0                           cnn_layers   python  keras-team/keras   \n",
       "1         EvaluateInputTensor.__init__   python  keras-team/keras   \n",
       "2     EvaluateInputTensor.on_epoch_end   python  keras-team/keras   \n",
       "3              CharacterTable.__init__   python  keras-team/keras   \n",
       "4                CharacterTable.encode   python  keras-team/keras   \n",
       "5                CharacterTable.decode   python  keras-team/keras   \n",
       "6                      generate_movies   python  keras-team/keras   \n",
       "7                     preprocess_image   python  keras-team/keras   \n",
       "8                      deprocess_image   python  keras-team/keras   \n",
       "9                  eval_loss_and_grads   python  keras-team/keras   \n",
       "10                          resize_img   python  keras-team/keras   \n",
       "11                     gradient_ascent   python  keras-team/keras   \n",
       "12                            load_img   python  keras-team/keras   \n",
       "13                       get_cam_model   python  keras-team/keras   \n",
       "14                         postprocess   python  keras-team/keras   \n",
       "15                    preprocess_image   python  keras-team/keras   \n",
       "16                     deprocess_image   python  keras-team/keras   \n",
       "17                         gram_matrix   python  keras-team/keras   \n",
       "18                          style_loss   python  keras-team/keras   \n",
       "19                        content_loss   python  keras-team/keras   \n",
       "20                total_variation_loss   python  keras-team/keras   \n",
       "21                 eval_loss_and_grads   python  keras-team/keras   \n",
       "22                  Evaluator.__init__   python  keras-team/keras   \n",
       "23                      Evaluator.loss   python  keras-team/keras   \n",
       "24                     Evaluator.grads   python  keras-team/keras   \n",
       "25                              squash   python  keras-team/keras   \n",
       "26                         margin_loss   python  keras-team/keras   \n",
       "27                    Capsule.__init__   python  keras-team/keras   \n",
       "28                       Capsule.build   python  keras-team/keras   \n",
       "29                        Capsule.call   python  keras-team/keras   \n",
       "...                                ...      ...               ...   \n",
       "1763             KerasClassifier.score   python  keras-team/keras   \n",
       "1764            KerasRegressor.predict   python  keras-team/keras   \n",
       "1765              KerasRegressor.score   python  keras-team/keras   \n",
       "1766                         load_data   python  keras-team/keras   \n",
       "1767                         load_data   python  keras-team/keras   \n",
       "1768                    get_word_index   python  keras-team/keras   \n",
       "1769                         load_data   python  keras-team/keras   \n",
       "1770                         load_data   python  keras-team/keras   \n",
       "1771                         load_data   python  keras-team/keras   \n",
       "1772                        load_batch   python  keras-team/keras   \n",
       "1773                         load_data   python  keras-team/keras   \n",
       "1774                         load_data   python  keras-team/keras   \n",
       "1775                    get_word_index   python  keras-team/keras   \n",
       "1776            get_function_signature   python  keras-team/keras   \n",
       "1777               get_class_signature   python  keras-team/keras   \n",
       "1778            post_process_signature   python  keras-team/keras   \n",
       "1779                 clean_module_name   python  keras-team/keras   \n",
       "1780              class_to_source_link   python  keras-team/keras   \n",
       "1781                      code_snippet   python  keras-team/keras   \n",
       "1782              count_leading_spaces   python  keras-team/keras   \n",
       "1783                process_list_block   python  keras-team/keras   \n",
       "1784                 process_docstring   python  keras-team/keras   \n",
       "1785             add_np_implementation   python  keras-team/keras   \n",
       "1786                         read_file   python  keras-team/keras   \n",
       "1787             collect_class_methods   python  keras-team/keras   \n",
       "1788                   render_function   python  keras-team/keras   \n",
       "1789                    read_page_data   python  keras-team/keras   \n",
       "1790              get_module_docstring   python  keras-team/keras   \n",
       "1791                     copy_examples   python  keras-team/keras   \n",
       "1792                          generate   python  keras-team/keras   \n",
       "\n",
       "                                             parameters  \\\n",
       "0                                       (x_train_input)   \n",
       "1     (self, model, steps, metrics_prefix='val', ver...   \n",
       "2                                (self, epoch, logs={})   \n",
       "3                                         (self, chars)   \n",
       "4                                   (self, C, num_rows)   \n",
       "5                           (self, x, calc_argmax=True)   \n",
       "6                         (n_samples=1200, n_frames=15)   \n",
       "7                                          (image_path)   \n",
       "8                                                   (x)   \n",
       "9                                                   (x)   \n",
       "10                                          (img, size)   \n",
       "11                 (x, iterations, step, max_loss=None)   \n",
       "12                   (fname, input_size, preprocess_fn)   \n",
       "13    (model_class,\\n                  input_size=22...   \n",
       "14                               (preds, cams, top_k=1)   \n",
       "15                                         (image_path)   \n",
       "16                                                  (x)   \n",
       "17                                                  (x)   \n",
       "18                                 (style, combination)   \n",
       "19                                  (base, combination)   \n",
       "20                                                  (x)   \n",
       "21                                                  (x)   \n",
       "22                                               (self)   \n",
       "23                                            (self, x)   \n",
       "24                                            (self, x)   \n",
       "25                                         (x, axis=-1)   \n",
       "26                                     (y_true, y_pred)   \n",
       "27    (self,\\n                 num_capsule,\\n       ...   \n",
       "28                                  (self, input_shape)   \n",
       "29                             (self, inputs, **kwargs)   \n",
       "...                                                 ...   \n",
       "1763                             (self, x, y, **kwargs)   \n",
       "1764                                (self, x, **kwargs)   \n",
       "1765                             (self, x, y, **kwargs)   \n",
       "1766                                                 ()   \n",
       "1767  (path='imdb.npz', num_words=None, skip_top=0,\\...   \n",
       "1768                      (path='imdb_word_index.json')   \n",
       "1769                                (label_mode='fine')   \n",
       "1770  (path='boston_housing.npz', test_split=0.2, se...   \n",
       "1771                                 (path='mnist.npz')   \n",
       "1772                        (fpath, label_key='labels')   \n",
       "1773                                                 ()   \n",
       "1774  (path='reuters.npz', num_words=None, skip_top=...   \n",
       "1775                   (path='reuters_word_index.json')   \n",
       "1776                            (function, method=True)   \n",
       "1777                                              (cls)   \n",
       "1778                                        (signature)   \n",
       "1779                                             (name)   \n",
       "1780                                              (cls)   \n",
       "1781                                          (snippet)   \n",
       "1782                                                (s)   \n",
       "1783  (docstring, starting_point, section_end,\\n    ...   \n",
       "1784                                        (docstring)   \n",
       "1785                              (function, docstring)   \n",
       "1786                                             (path)   \n",
       "1787                                     (cls, methods)   \n",
       "1788                            (function, method=True)   \n",
       "1789                                  (page_data, type)   \n",
       "1790                                         (filepath)   \n",
       "1791                    (examples_dir, destination_dir)   \n",
       "1792                                      (sources_dir)   \n",
       "\n",
       "                                   path  \\\n",
       "0            examples/mnist_tfrecord.py   \n",
       "1            examples/mnist_tfrecord.py   \n",
       "2            examples/mnist_tfrecord.py   \n",
       "3              examples/addition_rnn.py   \n",
       "4              examples/addition_rnn.py   \n",
       "5              examples/addition_rnn.py   \n",
       "6                 examples/conv_lstm.py   \n",
       "7                examples/deep_dream.py   \n",
       "8                examples/deep_dream.py   \n",
       "9                examples/deep_dream.py   \n",
       "10               examples/deep_dream.py   \n",
       "11               examples/deep_dream.py   \n",
       "12    examples/class_activation_maps.py   \n",
       "13    examples/class_activation_maps.py   \n",
       "14    examples/class_activation_maps.py   \n",
       "15    examples/neural_style_transfer.py   \n",
       "16    examples/neural_style_transfer.py   \n",
       "17    examples/neural_style_transfer.py   \n",
       "18    examples/neural_style_transfer.py   \n",
       "19    examples/neural_style_transfer.py   \n",
       "20    examples/neural_style_transfer.py   \n",
       "21    examples/neural_style_transfer.py   \n",
       "22    examples/neural_style_transfer.py   \n",
       "23    examples/neural_style_transfer.py   \n",
       "24    examples/neural_style_transfer.py   \n",
       "25      examples/cifar10_cnn_capsule.py   \n",
       "26      examples/cifar10_cnn_capsule.py   \n",
       "27      examples/cifar10_cnn_capsule.py   \n",
       "28      examples/cifar10_cnn_capsule.py   \n",
       "29      examples/cifar10_cnn_capsule.py   \n",
       "...                                 ...   \n",
       "1763     keras/wrappers/scikit_learn.py   \n",
       "1764     keras/wrappers/scikit_learn.py   \n",
       "1765     keras/wrappers/scikit_learn.py   \n",
       "1766    keras/datasets/fashion_mnist.py   \n",
       "1767             keras/datasets/imdb.py   \n",
       "1768             keras/datasets/imdb.py   \n",
       "1769         keras/datasets/cifar100.py   \n",
       "1770   keras/datasets/boston_housing.py   \n",
       "1771            keras/datasets/mnist.py   \n",
       "1772            keras/datasets/cifar.py   \n",
       "1773          keras/datasets/cifar10.py   \n",
       "1774          keras/datasets/reuters.py   \n",
       "1775          keras/datasets/reuters.py   \n",
       "1776                    docs/autogen.py   \n",
       "1777                    docs/autogen.py   \n",
       "1778                    docs/autogen.py   \n",
       "1779                    docs/autogen.py   \n",
       "1780                    docs/autogen.py   \n",
       "1781                    docs/autogen.py   \n",
       "1782                    docs/autogen.py   \n",
       "1783                    docs/autogen.py   \n",
       "1784                    docs/autogen.py   \n",
       "1785                    docs/autogen.py   \n",
       "1786                    docs/autogen.py   \n",
       "1787                    docs/autogen.py   \n",
       "1788                    docs/autogen.py   \n",
       "1789                    docs/autogen.py   \n",
       "1790                    docs/autogen.py   \n",
       "1791                    docs/autogen.py   \n",
       "1792                    docs/autogen.py   \n",
       "\n",
       "                                       return_statement  \\\n",
       "0                                    return x_train_out   \n",
       "1                                                         \n",
       "2                                                         \n",
       "3                                                         \n",
       "4                                              return x   \n",
       "5       return ''.join(self.indices_char[x] for x in x)   \n",
       "6                   return noisy_movies, shifted_movies   \n",
       "7                                            return img   \n",
       "8                                              return x   \n",
       "9                        return loss_value, grad_values   \n",
       "10     return scipy.ndimage.zoom(img, factors, order=1)   \n",
       "11                                             return x   \n",
       "12             return imgs, original_img, original_size   \n",
       "13                                     return cam_model   \n",
       "14                          return class_activation_map   \n",
       "15                                           return img   \n",
       "16                                             return x   \n",
       "17                                          return gram   \n",
       "18    return K.sum(K.square(S - C)) / (4.0 * (channe...   \n",
       "19           return K.sum(K.square(combination - base))   \n",
       "20                     return K.sum(K.pow(a + b, 1.25))   \n",
       "21                       return loss_value, grad_values   \n",
       "22                                                        \n",
       "23                               return self.loss_value   \n",
       "24                                   return grad_values   \n",
       "25                                     return scale * x   \n",
       "26    return K.sum(y_true * K.square(K.relu(1 - marg...   \n",
       "27                                                        \n",
       "28                                                        \n",
       "29                                             return o   \n",
       "...                                                 ...   \n",
       "1763                                                      \n",
       "1764  return np.squeeze(self.model.predict(x, **kwar...   \n",
       "1765                                       return -loss   \n",
       "1766        return (x_train, y_train), (x_test, y_test)   \n",
       "1767        return (x_train, y_train), (x_test, y_test)   \n",
       "1768                                                      \n",
       "1769        return (x_train, y_train), (x_test, y_test)   \n",
       "1770        return (x_train, y_train), (x_test, y_test)   \n",
       "1771        return (x_train, y_train), (x_test, y_test)   \n",
       "1772                                return data, labels   \n",
       "1773        return (x_train, y_train), (x_test, y_test)   \n",
       "1774        return (x_train, y_train), (x_test, y_test)   \n",
       "1775                                                      \n",
       "1776           return post_process_signature(signature)   \n",
       "1777     return post_process_signature(class_signature)   \n",
       "1778                                   return signature   \n",
       "1779                                        return name   \n",
       "1780                  return '[[source]](' + link + ')'   \n",
       "1781                                      return result   \n",
       "1782                                                      \n",
       "1783                            return docstring, block   \n",
       "1784                                   return docstring   \n",
       "1785  return docstring.replace('{{np_implementation}...   \n",
       "1786                                                      \n",
       "1787                                     return methods   \n",
       "1788                      return '\\n\\n'.join(subblocks)   \n",
       "1789                                        return data   \n",
       "1790                return docstring, co.co_firstlineno   \n",
       "1791                                                      \n",
       "1792                                                      \n",
       "\n",
       "                                           sha  \\\n",
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       "\n",
       "                                                    url  \n",
       "0     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "2     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "3     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "4     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "5     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "6     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "7     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "8     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "9     https://github.com/keras-team/keras/blob/0fc33...  \n",
       "10    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "11    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "12    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "13    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "14    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "15    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "16    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "17    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "18    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "19    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "20    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "21    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "22    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "23    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "24    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "25    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "26    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "27    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "28    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "29    https://github.com/keras-team/keras/blob/0fc33...  \n",
       "...                                                 ...  \n",
       "1763  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1764  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1765  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1766  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1767  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1768  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1769  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1770  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1771  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1772  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1773  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1774  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1775  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1776  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1777  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1778  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1779  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1780  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1781  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1782  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1783  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1784  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1785  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1786  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1787  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1788  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1789  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1790  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1791  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "1792  https://github.com/keras-team/keras/blob/0fc33...  \n",
       "\n",
       "[1793 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(definitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "library_candidates = {}\n",
    "library_candidates[dependee.split('/')[-1]] = definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependent = 'eriklindernoren/Keras-GAN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls, edges = processor.process_dent(dependent, ext=LANGUAGE_METADATA[language]['ext'], library_candidates=library_candidates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>argument_list</th>\n",
       "      <th>identifier</th>\n",
       "      <th>language</th>\n",
       "      <th>nwo</th>\n",
       "      <th>path</th>\n",
       "      <th>sha</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(0.0002, 0.5)</td>\n",
       "      <td>Adam</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(layer_input)</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(filters, kernel_size=f_size, strides=2, paddi...</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(d)</td>\n",
       "      <td>LeakyReLU</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(alpha=0.2)</td>\n",
       "      <td>LeakyReLU</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(layer_input)</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(size=2)</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(u)</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(filters, kernel_size=f_size, strides=1, paddi...</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(u)</td>\n",
       "      <td>Dropout</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(dropout_rate)</td>\n",
       "      <td>Dropout</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>([u, skip_input])</td>\n",
       "      <td>Concatenate</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>()</td>\n",
       "      <td>Concatenate</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(u6)</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(size=2)</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(u7)</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(self.channels, kernel_size=4, strides=1,\\n   ...</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(layer_input)</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(filters, kernel_size=f_size, strides=2, paddi...</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(d)</td>\n",
       "      <td>LeakyReLU</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(alpha=0.2)</td>\n",
       "      <td>LeakyReLU</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(d4)</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(1, kernel_size=4, strides=1, padding='same')</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>discogan/discogan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(0.0002, 0.5)</td>\n",
       "      <td>Adam</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>dcgan/dcgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>()</td>\n",
       "      <td>Sequential</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>dcgan/dcgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(128 * 7 * 7, activation=\"relu\", input_dim=sel...</td>\n",
       "      <td>Dense</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>dcgan/dcgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>((7, 7, 128))</td>\n",
       "      <td>Reshape</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>dcgan/dcgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>()</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>dcgan/dcgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(128, kernel_size=3, padding=\"same\")</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>dcgan/dcgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(momentum=0.8)</td>\n",
       "      <td>BatchNormalization</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>dcgan/dcgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>(alpha=0.2)</td>\n",
       "      <td>LeakyReLU</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>(d)</td>\n",
       "      <td>BatchNormalization</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>(momentum=0.8)</td>\n",
       "      <td>BatchNormalization</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>(layer_input)</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>(size=2)</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>(u)</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>(filters, kernel_size=f_size, strides=1, paddi...</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>(u)</td>\n",
       "      <td>Dropout</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>(dropout_rate)</td>\n",
       "      <td>Dropout</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>(u)</td>\n",
       "      <td>BatchNormalization</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>(momentum=0.8)</td>\n",
       "      <td>BatchNormalization</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>([u, skip_input])</td>\n",
       "      <td>Concatenate</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>()</td>\n",
       "      <td>Concatenate</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>(u3)</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>(size=2)</td>\n",
       "      <td>UpSampling2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>(u4)</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>(self.channels, kernel_size=4, strides=1, padd...</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>()</td>\n",
       "      <td>Sequential</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>(64, kernel_size=4, strides=2, padding='same',...</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>(alpha=0.8)</td>\n",
       "      <td>LeakyReLU</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>(128, kernel_size=4, strides=2, padding='same')</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>(alpha=0.2)</td>\n",
       "      <td>LeakyReLU</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>(256, kernel_size=4, strides=2, padding='same')</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>(alpha=0.2)</td>\n",
       "      <td>LeakyReLU</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>(features)</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>(1, kernel_size=4, strides=1, padding='same')</td>\n",
       "      <td>Conv2D</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>(features)</td>\n",
       "      <td>Flatten</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>()</td>\n",
       "      <td>Flatten</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>(label)</td>\n",
       "      <td>Dense</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>(self.num_classes+1, activation=\"softmax\")</td>\n",
       "      <td>Dense</td>\n",
       "      <td>python</td>\n",
       "      <td>eriklindernoren/Keras-GAN</td>\n",
       "      <td>ccgan/ccgan.py</td>\n",
       "      <td>44d3320e84ca00071de8a5c0fb4566d10486bb1d</td>\n",
       "      <td>https://github.com/eriklindernoren/Keras-GAN/b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>651 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         argument_list          identifier  \\\n",
       "0                                        (0.0002, 0.5)                Adam   \n",
       "1                                        (layer_input)              Conv2D   \n",
       "2    (filters, kernel_size=f_size, strides=2, paddi...              Conv2D   \n",
       "3                                                  (d)           LeakyReLU   \n",
       "4                                          (alpha=0.2)           LeakyReLU   \n",
       "5                                        (layer_input)        UpSampling2D   \n",
       "6                                             (size=2)        UpSampling2D   \n",
       "7                                                  (u)              Conv2D   \n",
       "8    (filters, kernel_size=f_size, strides=1, paddi...              Conv2D   \n",
       "9                                                  (u)             Dropout   \n",
       "10                                      (dropout_rate)             Dropout   \n",
       "11                                   ([u, skip_input])         Concatenate   \n",
       "12                                                  ()         Concatenate   \n",
       "13                                                (u6)        UpSampling2D   \n",
       "14                                            (size=2)        UpSampling2D   \n",
       "15                                                (u7)              Conv2D   \n",
       "16   (self.channels, kernel_size=4, strides=1,\\n   ...              Conv2D   \n",
       "17                                       (layer_input)              Conv2D   \n",
       "18   (filters, kernel_size=f_size, strides=2, paddi...              Conv2D   \n",
       "19                                                 (d)           LeakyReLU   \n",
       "20                                         (alpha=0.2)           LeakyReLU   \n",
       "21                                                (d4)              Conv2D   \n",
       "22       (1, kernel_size=4, strides=1, padding='same')              Conv2D   \n",
       "23                                       (0.0002, 0.5)                Adam   \n",
       "24                                                  ()          Sequential   \n",
       "25   (128 * 7 * 7, activation=\"relu\", input_dim=sel...               Dense   \n",
       "26                                       ((7, 7, 128))             Reshape   \n",
       "27                                                  ()        UpSampling2D   \n",
       "28                (128, kernel_size=3, padding=\"same\")              Conv2D   \n",
       "29                                      (momentum=0.8)  BatchNormalization   \n",
       "..                                                 ...                 ...   \n",
       "621                                        (alpha=0.2)           LeakyReLU   \n",
       "622                                                (d)  BatchNormalization   \n",
       "623                                     (momentum=0.8)  BatchNormalization   \n",
       "624                                      (layer_input)        UpSampling2D   \n",
       "625                                           (size=2)        UpSampling2D   \n",
       "626                                                (u)              Conv2D   \n",
       "627  (filters, kernel_size=f_size, strides=1, paddi...              Conv2D   \n",
       "628                                                (u)             Dropout   \n",
       "629                                     (dropout_rate)             Dropout   \n",
       "630                                                (u)  BatchNormalization   \n",
       "631                                     (momentum=0.8)  BatchNormalization   \n",
       "632                                  ([u, skip_input])         Concatenate   \n",
       "633                                                 ()         Concatenate   \n",
       "634                                               (u3)        UpSampling2D   \n",
       "635                                           (size=2)        UpSampling2D   \n",
       "636                                               (u4)              Conv2D   \n",
       "637  (self.channels, kernel_size=4, strides=1, padd...              Conv2D   \n",
       "638                                                 ()          Sequential   \n",
       "639  (64, kernel_size=4, strides=2, padding='same',...              Conv2D   \n",
       "640                                        (alpha=0.8)           LeakyReLU   \n",
       "641    (128, kernel_size=4, strides=2, padding='same')              Conv2D   \n",
       "642                                        (alpha=0.2)           LeakyReLU   \n",
       "643    (256, kernel_size=4, strides=2, padding='same')              Conv2D   \n",
       "644                                        (alpha=0.2)           LeakyReLU   \n",
       "645                                         (features)              Conv2D   \n",
       "646      (1, kernel_size=4, strides=1, padding='same')              Conv2D   \n",
       "647                                         (features)             Flatten   \n",
       "648                                                 ()             Flatten   \n",
       "649                                            (label)               Dense   \n",
       "650         (self.num_classes+1, activation=\"softmax\")               Dense   \n",
       "\n",
       "    language                        nwo                  path  \\\n",
       "0     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "1     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "2     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "3     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "4     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "5     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "6     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "7     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "8     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "9     python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "10    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "11    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "12    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "13    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "14    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "15    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "16    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "17    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "18    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "19    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "20    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "21    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "22    python  eriklindernoren/Keras-GAN  discogan/discogan.py   \n",
       "23    python  eriklindernoren/Keras-GAN        dcgan/dcgan.py   \n",
       "24    python  eriklindernoren/Keras-GAN        dcgan/dcgan.py   \n",
       "25    python  eriklindernoren/Keras-GAN        dcgan/dcgan.py   \n",
       "26    python  eriklindernoren/Keras-GAN        dcgan/dcgan.py   \n",
       "27    python  eriklindernoren/Keras-GAN        dcgan/dcgan.py   \n",
       "28    python  eriklindernoren/Keras-GAN        dcgan/dcgan.py   \n",
       "29    python  eriklindernoren/Keras-GAN        dcgan/dcgan.py   \n",
       "..       ...                        ...                   ...   \n",
       "621   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "622   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "623   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "624   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "625   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "626   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "627   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "628   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "629   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "630   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "631   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "632   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "633   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "634   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "635   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "636   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "637   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "638   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "639   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "640   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "641   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "642   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "643   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "644   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "645   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "646   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "647   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "648   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "649   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "650   python  eriklindernoren/Keras-GAN        ccgan/ccgan.py   \n",
       "\n",
       "                                          sha  \\\n",
       "0    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "1    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "2    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "3    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "4    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "5    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "6    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "7    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "8    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "9    44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "10   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "11   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "12   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "13   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "14   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "15   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "16   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "17   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "18   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "19   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "20   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "21   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "22   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "23   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "24   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "25   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "26   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "27   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "28   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "29   44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "..                                        ...   \n",
       "621  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "622  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "623  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "624  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "625  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "626  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "627  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "628  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "629  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "630  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "631  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "632  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "633  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "634  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "635  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "636  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "637  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "638  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "639  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "640  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "641  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "642  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "643  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "644  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "645  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "646  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "647  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "648  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "649  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "650  44d3320e84ca00071de8a5c0fb4566d10486bb1d   \n",
       "\n",
       "                                                   url  \n",
       "0    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "1    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "2    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "3    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "4    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "5    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "6    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "7    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "8    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "9    https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "10   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "11   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "12   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "13   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "14   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "15   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "16   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "17   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "18   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "19   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "20   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "21   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "22   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "23   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "24   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "25   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "26   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "27   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "28   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "29   https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "..                                                 ...  \n",
       "621  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "622  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "623  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "624  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "625  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "626  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "627  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "628  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "629  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "630  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "631  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "632  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "633  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "634  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "635  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "636  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "637  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "638  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "639  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "640  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "641  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "642  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "643  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "644  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "645  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "646  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "647  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "648  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "649  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "650  https://github.com/eriklindernoren/Keras-GAN/b...  \n",
       "\n",
       "[651 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L81-L81',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L93-L93',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/merge.py#L340-L344'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L93-L93',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/merge.py#L340-L344'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L116-L116',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L116-L116',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L117-L117',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L117-L117',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L125-L125',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L125-L125',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L126-L126',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L126-L126',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L128-L128',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L128-L128',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L135-L135',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/merge.py#L340-L344'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L135-L135',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/merge.py#L340-L344'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L142-L142',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pix2pix/pix2pix.py#L142-L142',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L47-L47',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L122-L122',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L122-L122',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L123-L123',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L123-L123',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L124-L124',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L124-L124',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L125-L125',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L125-L125',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L126-L126',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L126-L126',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L132-L132',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L132-L132',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L133-L133',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L133-L133',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L134-L134',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L134-L134',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L141-L141',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L141-L141',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L142-L142',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L142-L142',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L150-L150',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L150-L150',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L151-L151',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L151-L151',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L159-L159',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L159-L159',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L167-L167',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L167-L167',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L168-L168',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L168-L168',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L170-L170',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L170-L170',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L185-L185',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L185-L185',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L186-L186',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L186-L186',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L187-L187',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/srgan/srgan.py#L187-L187',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L27-L27',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L70-L70',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L71-L71',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L72-L72',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L73-L73',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L74-L74',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L75-L75',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L76-L76',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L77-L77',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L79-L79',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L80-L80',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L95-L95',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L96-L96',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L97-L97',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L98-L98',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L99-L99',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2204-L2228'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L102-L102',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L103-L103',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L104-L104',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L105-L105',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L106-L106',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L108-L108',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L109-L109',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L110-L110',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L111-L111',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L112-L112',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L117-L117',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L117-L117',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L120-L120',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L120-L120',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L121-L121',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/infogan/infogan.py#L121-L121',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L25-L25',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L54-L54',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L56-L56',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L57-L57',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L58-L58',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L59-L59',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L60-L60',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L62-L62',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L63-L63',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L64-L64',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L65-L65',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L66-L66',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L77-L77',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L79-L79',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L80-L80',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L81-L81',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L82-L82',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/gan/gan.py#L84-L84',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L30-L30',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L70-L70',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L71-L71',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L72-L72',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L73-L73',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L74-L74',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L76-L76',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L77-L77',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L81-L81',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L82-L82',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L84-L84',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L86-L86',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L87-L87',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L103-L103',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L104-L104',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L105-L105',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L106-L106',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L108-L108',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L109-L109',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L110-L110',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L111-L111',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L112-L112',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/context_encoder/context_encoder.py#L113-L113',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L27-L27',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L70-L70',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L71-L71',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L72-L72',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L73-L73',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L86-L86',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L87-L87',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L105-L105',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L105-L105',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L108-L108',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L108-L108',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L109-L109',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L109-L109',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L110-L110',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L110-L110',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L111-L111',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L111-L111',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L112-L112',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L112-L112',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L113-L113',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L113-L113',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L114-L114',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L114-L114',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L115-L115',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L115-L115',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L116-L116',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bigan/bigan.py#L116-L116',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L27-L27',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L75-L75',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L75-L75',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L76-L76',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L76-L76',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L77-L77',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L77-L77',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L79-L79',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L79-L79',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L82-L82',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L82-L82',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L84-L84',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L84-L84',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L86-L86',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L86-L86',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L98-L98',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L99-L99',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L102-L102',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L103-L103',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L109-L109',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L109-L109',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L111-L111',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cogan/cogan.py#L111-L111',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L30-L30',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L238-L249'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L62-L62',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L70-L70',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L71-L71',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L72-L72',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L73-L73',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L74-L74',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L75-L75',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L86-L86',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2204-L2228'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L93-L93',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L94-L94',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L95-L95',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L96-L96',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L97-L97',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L98-L98',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L99-L99',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L102-L102',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L103-L103',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L104-L104',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan/wgan.py#L105-L105',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L45-L45',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L106-L106',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L106-L106',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L113-L113',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L113-L113',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L114-L114',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L114-L114',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L116-L116',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L116-L116',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L118-L118',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/merge.py#L340-L344'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L118-L118',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/merge.py#L340-L344'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L135-L135',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L135-L135',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cyclegan/cyclegan.py#L145-L145',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L79-L79',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L80-L80',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L81-L81',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L82-L82',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L84-L84',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L86-L86',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L87-L87',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L102-L102',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L103-L103',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L104-L104',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L105-L105',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L106-L106',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dualgan/dualgan.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L27-L27',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L80-L80',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L81-L81',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L82-L82',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L84-L84',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L96-L96',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L98-L98',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L99-L99',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/aae/aae.py#L102-L102',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L43-L43',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L99-L99',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L99-L99',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L106-L106',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L106-L106',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L115-L115',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L115-L115',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L116-L116',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L116-L116',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L128-L128',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L128-L128',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L136-L136',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L136-L136',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L137-L137',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L137-L137',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L150-L150',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L150-L150',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L150-L150',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/pixelda/pixelda.py#L150-L150',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L25-L25',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L57-L57',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L59-L59',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L60-L60',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L61-L61',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L62-L62',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L75-L75',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L75-L75',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L75-L75',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/embeddings.py#L80-L103'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L75-L75',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/embeddings.py#L80-L103'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L84-L84',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L86-L86',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L87-L87',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L93-L93',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L94-L94',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/embeddings.py#L80-L103'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/embeddings.py#L80-L103'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/cgan/cgan.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L27-L27',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L55-L55',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L57-L57',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L58-L58',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L59-L59',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L60-L60',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L61-L61',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L62-L62',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L80-L80',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L81-L81',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L82-L82',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L84-L84',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/bgan/bgan.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L27-L27',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L58-L58',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L60-L60',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L61-L61',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L62-L62',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L70-L70',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L71-L71',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L72-L72',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L86-L86',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L87-L87',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L88-L88',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2204-L2228'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L93-L93',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L94-L94',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L95-L95',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L96-L96',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L97-L97',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L98-L98',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L99-L99',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L107-L107',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L108-L108',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/sgan/sgan.py#L108-L108',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L25-L25',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L54-L54',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L56-L56',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L57-L57',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L58-L58',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L59-L59',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L60-L60',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L61-L61',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L62-L62',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L77-L77',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L79-L79',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L80-L80',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L81-L81',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L82-L82',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L83-L83',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/lsgan/lsgan.py#L85-L85',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L25-L25',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/optimizers.py#L455-L468'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L58-L58',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L60-L60',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L61-L61',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L346-L348'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L62-L62',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L63-L63',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L64-L64',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L65-L65',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L66-L66',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L67-L67',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2014-L2021'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L68-L68',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L69-L69',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L70-L70',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L71-L71',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L72-L72',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/embeddings.py#L80-L103'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L78-L78',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/embeddings.py#L80-L103'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L87-L87',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/engine/sequential.py#L87-L94'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L89-L89',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L90-L90',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L91-L91',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L92-L92',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L93-L93',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L2204-L2228'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L94-L94',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L95-L95',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L96-L96',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L97-L97',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L98-L98',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L99-L99',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L100-L100',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/normalization.py#L61-L91'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L101-L101',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/convolutional.py#L451-L484'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L102-L102',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/advanced_activations.py#L42-L45'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L103-L103',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L103-L108'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L105-L105',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L492-L495'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L114-L114',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L114-L114',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L115-L115',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/acgan/acgan.py#L115-L115',\n",
       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885'),\n",
       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan_gp/wgan_gp.py#L42-L42',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan_gp/wgan_gp.py#L131-L131',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan_gp/wgan_gp.py#L132-L132',\n",
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       " ('https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/wgan_gp/wgan_gp.py#L133-L133',\n",
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       "  'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L860-L885')]"
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